Artificial intelligence has enabled computer systems to conduct conversations, either textual or computer-generated speech, with humans. Most people encounter these conversant computer systems in the form of customer service chatbots. These customer service oriented chatbots may use leading questions to elicit an appropriate response from the human participant. As a result, the conversation while straight forward has a “mechanical” quality to it that may be off-putting to some of the human participants.
While canned expressions, such as “I hope you are having a good day” or for repeat callers, “Thank you contacting us again,” may provide a more realistic feel to the conversation, these canned expressions may only make the conversation flow seem forced and disingenuous since there is no context in the conversation for such canned expressions. Accordingly, a problem with chatbot training is that the chatbot models often default to the most generic speech patterns due to a lack of context obtained from the training data.
There is a need for ways to mitigate the mechanical, or “machine-like” speech quality of current chatbot conversations and provide more realistic conversational flow and context by referencing real-life aspects of a human participant.